Method and apparatus for determining a bridged tap length

ABSTRACT

Embodiments relate to an apparatus ( 5, 7 ) comprising means ( 31, 32 , P) configured for:
         generating (S 1 ) a dataset specifying, for a plurality of communication lines:
           a channel frequency response of a communication line having one or two bridged taps, and   a set of M lengths of bridged taps associated with the communication line, with M greater than one,   
           training (S 2 ), based on the dataset, a machine learning model configured for determining, based on the channel frequency response of a communication line, a set of M lengths of bridged taps associated with the communication line.

FIELD OF THE INVENTION

The present invention relates to the field of telecommunication. Inparticular, the present invention relates to a method and an apparatusfor determining the length of a bridged tap in a communication line.

BACKGROUND

Communication lines, for example used for DSL communication, sometimespresent a special topological configuration: A double path, also knownas bridged tap, is connected to the main line. From an operatorperspective, the presence of one or more bridged taps affects thequality of service. This occurs as speed reduction, presence oferroneous service, some connection losses, etc. There is therefore aneed for detecting the bridged taps. Moreover, operators are in seek forsuitable recommendations, i.e. for insights on the most appropriateactions to take in order to restore the end-user's experience as well asa high-level of satisfaction within the shortest time. Under thatcontext, estimating accurately the lengths of the detected bridged tapsallows the operator to state about the domain of application of thesebridged taps (e.g. field/street vs. home) or even make a distinctionbetween cables within buildings/home. As such, this offers thepossibility to address the domain separation problem, very important inthe competitive context of service providers and copper owner.

The channel frequency response of a communication line represents theattenuation of the medium on the emitted signal for respectivefrequencies. In a multi-tones communication system such as DSL, it maybe used to fill an appropriate number of bits over the differentfrequency carriers. Also, analyzing the channel frequency response of acommunication line allows detecting and characterizing impairmentsaffecting the communication line. For example, empirical analysis of thechannel frequency response allows estimating the length of a bridgedtap.

SUMMARY

It is thus an object of embodiments of the present invention to proposea method and an apparatus for estimating bridged tap length, which donot show the inherent shortcomings of the prior art.

Accordingly, embodiments relate to an apparatus comprising meansconfigured for:

-   -   generating a dataset specifying, for a plurality of        communication lines:    -   a channel frequency response of a communication line having one        or two bridged taps, and    -   a set of M lengths of bridged taps associated with the        communication line, with M greater than one,    -   training, based on the dataset, a machine learning model        configured for determining, based on the channel frequency        response of a communication line, a set of M lengths of bridged        taps associated with the communication line.

In some embodiments, training the machine leaning model comprisesupdating parameters of the machine learning model based on an errorrepresentative of a relative comparison between target lengths andpredicted lengths.

In some embodiments, training the machine leaning model comprisesupdating parameters of the machine learning model based on an errorrepresentative of a comparison between target lengths and predictedlengths and of a length distribution associated with a communicationnetwork.

In some embodiments, said means are further configured for determiningsaid length distribution based on iteratively:

-   -   updating parameters of the machine learning model based on the        dataset and a current estimation of the length distribution,    -   determining lengths based on the updated machine learning model        and channel frequency responses of communication lines of a real        communication network, and    -   updating the current estimation of the length distribution based        on the determined lengths.

In some embodiments, generating the dataset comprises determiningchannel frequency responses based on circuit simulation.

In some embodiments, generating the dataset comprises determiningchannel frequency responses for a plurality of communication lineshaving a single wire bridged tap and for a plurality of communicationlines having a double wire bridged tap.

In some embodiments, generating the dataset comprises determiningchannel frequency responses for a plurality of communication lineshaving an open-ended bridged tap and for a plurality of communicationlines having a close-ended bridged tap.

In some embodiments, generating the dataset comprises determiningchannel frequency responses for a plurality of communication lineshaving only one bridged tap and for a plurality of communication lineshaving at least two bridged taps.

In some embodiments, the means are further configured for:

-   -   obtaining the channel frequency response of a communication        line,    -   determining a set of M lengths of bridged taps associated with        the communication line, based on the channel frequency response        and the trained machine learning model.

Embodiments also relate to a computer-implemented method comprising:

-   -   generating a dataset specifying, for a plurality of        communication lines:    -   a channel frequency response of a communication line having one        or two bridged taps, and    -   a set of M lengths of bridged taps associated with the        communication line, with M greater than one,    -   training, based on the dataset, a machine learning model        configured for determining, based on the channel frequency        response of a communication line, a set of M lengths of bridged        taps associated with the communication line.

In some embodiments, the method comprises deploying the trained machinelearning model in another apparatus.

Embodiments also relate to an apparatus obtained by the above method,comprising means configured for:

-   -   obtaining the channel frequency response of a communication        line,    -   determining a set of M lengths of bridged taps associated with        the communication line, based on the channel frequency response        and the trained machine learning model.

Embodiments also relates to a computer-implemented method for monitoringa communication network, comprising:

-   -   obtaining the channel frequency response of a communication        line,    -   determining a set of M lengths of bridged taps associated with        the communication line, based on the channel frequency response        and a trained machine learning model generated by the method        above.

In some embodiments, said means include at least one processor and atleast one memory, the at least one memory storing instructions, the atleast one memory and the instructions being configured to, with the atleast one processor, cause the apparatus to at least in part perform thefunctions discussed above.

Embodiments also relate to a computer program comprising instructionsfor performing the method mentioned before when said instructions areexecuted by a computer. The computer program may be stored on a computerreadable medium. The computer readable medium may be a non-transitorycomputer readable medium.

The scope of protection sought for various embodiments of the inventionis set out by the independent claims. The embodiments and features, ifany, described in this specification that do not fall under the scope ofthe independent claims are to be interpreted as examples useful forunderstanding various embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects and features of the invention will becomemore apparent and the invention itself will be best understood byreferring to the following description of embodiments taken inconjunction with the accompanying drawings wherein:

FIG. 1 is a circuit diagram of a communication line having a bridgedtap,

FIG. 2 is a graph of the channel frequency response of the communicationline of FIG. 1,

FIG. 3 is a block diagram of communication network,

FIG. 4 is a flowchart of a method executed in the communication networkof FIG. 3,

FIG. 5 is a block diagram of a machine learning model configured forpredicting, based on the channel frequency response of a communicationline, the length of bridged taps connected to the communication line,

FIG. 6 is a flowchart of a method executed in the communication networkof FIG. 3, and

FIG. 7 is a structural view of an apparatus used in the network of FIG.3.

DESCRIPTION OF EMBODIMENTS

FIG. 1 is a circuit diagram of a communication line 10 having a bridgedtap 11. The communication line 10 comprises a pair of wires extendingbetween two communication nodes. For example, the communication line 10extends between an access node 12 located in a central office or streetcabinet of a network operator and a customer premises equipment 13located in a customer building, for example a house or an office. Theaccess node 12 and the customer premises equipment 13 may use DSLtechnology, for example ADSL, VDSL2, G.Fast . . . for data communicationover the communication line 10.

The bridged tap 11 comprises a wire or a pair of wires connected to thecommunication line 10. The bridged tap 11 may present variousconfiguration based on:

-   -   located in a network operator domain (e.g. central office), in        the street/field domain, or in the customer domain.    -   Two wires or single wire    -   Termination: open-ended or connected to a device, e.g. a phone,    -   Length    -   Type of cable (gauge/insulator), impedance    -   . . .

The bridged tap 11 affects the communication over the communication line10, for example in terms of speed reduction, presence of erroneousservice, some connection losses, etc. It is therefore desirable for anetwork operator to be able to detect that a communication line isaffected by one or more bridged taps, and to characterize the bridgedtaps, in particular to estimate their lengths.

From a proactive network wide perspective, operators are willing toprioritize their field interventions to remove impairments on DSL lineswhere they can ensure that removing the impairment will significantlyimprove the line performance. In this context, using a reliable methodto estimate the bridged tap length and ranking/sorting the resultsnetwork wide will improve customer strategy on the next best action toimprove their network.

From a reactive single DSL line scenario (Helpdesk call), improving theaccuracy of the bridged tap length estimation will permit the operatorto better know where in the network the impairment has the mostprobability to occur. And as such, better manage the field intervention.

FIG. 2 is a graph of a channel frequency response of a communicationline 10 affected by a bridged tap 11. More specifically, the example ofFIG. 2 relates to a Digital Subscriber Line (DSL). The curve is commonlyexpressed in dB and called H log in the DSL context, and depends on manytopological factors, such as the loop length, the wire gauge (lineimpedance), the insulation type, the connector properties . . . . Atypical format for H log is expressed in dB over 512 groups offrequencies.

The H log curve 14 is sensitive to the presence of any impairment(s) or,in general, to the presence of any unexpected topological configuration.In particular, as can be seen on FIG. 2, in the presence of a bridgedtap 11, such H log curve 14 presents some dips 15 in its shape. Thelocation and shape of the dips 15 depend on the bridged tapconfiguration, in particular its length.

FIG. 3 is a block diagram of a communication network 1 based on DSLtechnology. The communication network 1 comprises one or more accessnodes 2, one or more terminals 3, one or more communication lines 4 andan apparatus 5.

An access node 2 is for example a DSLAM including a plurality of DSLmodems and is connected to one or more terminals 3 by respectivecommunication lines 4. A terminal 3 is for example a Customer PremisesEquipment including a DSL modem. An access node 2 and a terminal 3 useDSL technology for communication over a communication line 4. Acommunication line 4 is for example a twisted copper pair.

An access node 2 and/or a terminal 3 may be configured for providingoperational data representative of the functioning of the DSL modems andof the communication line 4. The operational data may specify thechannel frequency response of the communication line 4, as measured bythe modems of the access node 2 and/or the terminal 3.

The apparatus 5 comprises a monitoring device 6 and a configurationdevice 7.

The monitoring device 6 may obtain the operational data provided by theaccess nodes 2 and/or terminals 3 and perform various monitoring andmanaging function based on the operational data, such as impairmentdetection, dynamic line configuration, status visualization andreporting . . . . In particular, for a communication line 4 which hasbeen detected has affected by one or more bridged taps, the monitoringdevice 6 may estimate or determine the lengths of bridged taps based onthe channel frequency response of the communication line 4. Techniquesfor length estimation are described in more details hereafter. In thisdescription, estimating the length, determining the length, determiningan estimation of the length, predicting the length . . . may be used asequivalent expressions.

The configuration device 7 may generate and provide to the monitoringdevice 6 a machine learning model configured for determining, based onthe channel frequency response of a communication line 4, a pair oflengths of bridged taps connected to the communication line 4.

Note that the distinction between the monitoring device 6 andconfiguration device 7 is functional. The monitoring device 6 andconfiguration device 7 may be computer-implemented devices. In someembodiments, the monitoring device 6 and the configuration device 7correspond to distinct computers or groups of computers. For example,the monitoring device 6 correspond to a server controlled by a networkoperator and the configuration device 7 correspond to a servercontrolled by a data analytics company. In other embodiments, themonitoring device 6 and the configuration device 7 may correspond to thesame computer or group of computers, for example to a server controlledby a network operator.

FIG. 4 is a flowchart of a method executed by the configuration device7.

The configuration device 7 generates or obtains a training dataset (StepS1). The training dataset specifies, for plurality of communicationlines affected by one or two bridged taps:

-   -   the channel frequency response, and    -   a pair of lengths of the bridged taps.

The pair of lengths is specified with the length L_(long) of the longerbridged tap first, followed by the length L_(short) of the shorterbridged tap: [L_(long) L_(short)] When the communication line has onlyone bridged tap, L_(short)=0.

More generally, in some embodiments, the training dataset specifies, forplurality of communication lines affected by one or more bridged taps:

-   -   the channel frequency response, and    -   a set of M lengths of the bridged taps, with M equal to or        greater than two. The set of M lengths may be ordered from        longer to shorter, and some of the lengths may be equal to zero        for a communication line with less than M bridged taps. The        following description will focus on embodiments with a pair of        lengths.

For example, various combination of bridged tap lengths andconfigurations are considered. The bridged tap configurations relate togauge/impedance, termination type, single or double wire . . . . For agiven combination of bridged taps lengths [L_(long) L_(short)] andconfiguration, the configuration device 7 uses circuit simulationtechniques to determine the channel frequency responses of thecommunication line. Circuit simulation is the task of determining one ormore properties of an electrical or electronical circuit based on themodel of the circuit.

A simulated channel frequency response and the corresponding pair oflengths [L_(long) L_(short)] form a training sample of the trainingdataset, where the pair of lengths [L_(long) L_(short)] may be regardedas the label for supervised training.

In some embodiments, data augmentation techniques are used forincreasing the number of training samples obtained based on simulationand covering more real-world situations. For example, a new trainingsample may be obtained by modifying the channel frequency response of acommunication line obtained by simulation, based on adding modemmeasurement effects typically observed in measured curves.

The channel frequency response may be expressed as a vector of N valuescorresponding to N frequencies or frequency groups. Typically, N=512 inthe DSL context. This is a commonly used format and may results from anaggregation of a larger curve, depending on the technologies. Forinstance, in VDSL2 17 Mhz, a measured H log of 4096 tones is aggregatedby a ratio of 1:8, producing a H log curve of 512 tone groups.

Then, the configuration device 7 trains a machine learning model basedon the generated training dataset (step S2).

The machine learning model is configured for determining a pair oflength [L_(long) L_(short)] based on the channel frequency response of acommunication line affected by one or more bridged taps. For example,the machine learning model takes as input a vector of size N specifyingthe channel frequency response, and outputs a vector [L_(long)L_(short)].

Various architecture of machine learning model may be used, and anexample is described later in reference to FIG. 5.

Training the machine learning model comprises setting the values ofparameters of the models, for example weights and biases, based on thetraining dataset. This may involve the stochastic gradient descentalgorithm or another training algorithm. The training samples may beprocessed by batches.

The stochastic gradient descent algorithm and other training algorithmsare based on determining an error and updating model parameters based onthe error. In some embodiments, training comprises determining, for abatch of one or more training samples, an error which depends on atleast one of:

-   -   A comparison between target lengths [L_(long)        L_(short)]_(target) and predicted length [L_(long)        L_(short)]_(predicted). Here, target refers to the values        specified by a training sample of the training dataset, and        predicted refer to the values determined by a forward pass of        the training process.    -   A distribution of bridged tap lengths in the communication        network.

For example, in some embodiments, the configuration device 7 uses theMean Square Error:

MSE _([batch])=mean_([batch])(([L _(long) L _(short)]_(predicted)−[L_(long) L _(short)]_(target))²)

In other embodiments, the configuration device 7 uses a relative MeanSquare Error, which allows to be more tolerant for long bridged tap.Indeed, from an operator perspective, in particular for the domainseparation task, a 2 meter error on a 40 meter bridged tap is moreacceptable than on a 5 meter bridged tap. The relative Mean Square Errormay be:

MSE _([batch])=mean_([batch])((([L _(long) L _(short)]_(predicted)−[L_(long) L _(short)]_(target))/[L _(long) L _(short)]_(target))²)

Other formulas for the relative means square error may process eachlength separately and sums the ratio, e.g.:

MSE _([batch])=mean_([batch])(((L _(long_predicted) −L _(long_target))/L_(long_target))² +L _(short_predicted) −L _(short_target))/L_(short_target))²

In other embodiments, the configuration device 7 uses a networkdependent error, which allows to match the bridged tap length accuracyto the bridged tap length dominantly present in the network. Forexample, the configuration device 7 considers the distribution of thebridged tap lengths in the network.

In some case, the configuration device 7 has some knowledge about thebridged tap lengths distribution, noted for example noted (dist(L) inthe case of a one-dimensional distribution or dist([L_(long) L_(short)])in the case of a 2D distribution. In such case, the network dependentMean Square Error may be:

MSE _([batch])=mean_([batch])(dist([L _(long) L _(short)]_(target))*([L_(long) L _(short)]_(predicted)[L _(long) L _(short)]_(target))²)

In other case, the configuration device 7 does not have a prioriknowledge of the distribution. In such case, the network dependent MeanSquare Error may be:

MSE _([batch])=mean_([batch])(w[L _(i,long) L _(i,short)]_(target)*([L_(i,long) L _(i,short)]_(predicted)−[L _(i,long) L_(i,short)]_(target))²)  (3)

where w is a weight related to the best value of the best knownestimation of a real network distribution at the given iteration.Indeed, as the tap length is not known in advance, as this is thepurpose of this method, an iterative method, for instance following theExpectation-Maximization (EM) approach, would be required and issuitable. In that approach, the configuration devices 7 obtains networkdata specifying the channel frequency responses for a plurality ofcommunication lines, in a real communication network. Then, theconfiguration device 7 iteratively:

-   -   update the parameters of the machine learning model based on a        batch from the training dataset, and an estimation of the length        distribution.    -   determines the lengths of the bridged taps based on channel        frequency responses and the updated machine learning model, and    -   update the estimation of the length distribution based on the        determined lengths.

For example, the weight w can get initialize to the 1.0 value, in orderto train a model without an a-priori knowledge on the tap length. Ateach following complete learning cycle, or even more efficient over thebatch iterations of a single learning event, the estimation of the taplength normalized distribution can get updated and refined. Formally, ateach iteration j,

wj[L _(long) L _(short)]alpha*dis _(network)([L _(long) L_(short)]_(test,j))+(alpha−1)*dis _(network)([L _(long) L_(short)]_(test,j-1)),

-   -   with 0.0<=alpha<=1.0 and sum(dis_(network))=1.0

After some iterations, wj converges, hence wj=w as there will not be anysignificant improvements over j.

Then, at least in embodiments wherein the configuration device 7 and themonitoring device 6 are distinct from each other, the configurationdevice 7 deploys the trained machine learning model in the monitoringdevice 6 (step S3). This involves sending data representative of thearchitecture and/or parameters of the trained machine learning model tothe monitoring device 6, thereby directly obtaining a monitoring device6 configured for using the trained machine learning model.

Generating a training dataset which is based on a variety of bridged taplengths and configurations allows to obtain a trained machine learningmodel capable of accurately estimating bridged tap lengths for aplurality of communication lines having different configurations (singlewire or double wire bridged tap, open or closed termination,impedance/gauge . . . ). Selecting a set of M lengths as label forsupervised training allows to cover configuration with one or morebridged taps, for example a pair of bridged taps. If the lengths in thetraining dataset are ordered, this information is also provided by themachine learning model.

Moreover, in some embodiments, training based on a relative error allowsto improve relative accuracy for both short and long bridged taps. Also,in some embodiments, training based on a network dependent error basedon a bridged tap length distribution allows to improve accuracy for mostfrequent bridged taps lengths.

Comparative tests have shown that the machine learning model determinesbridged tap lengths with improved accuracy and better confidence, incomparison with known empirical methods based on analysis of the shapeof a channel frequency response.

FIG. 5 is a block diagram of a machine learning model 20, which is usedby the apparatus 5 in some embodiments.

The machine learning model 20 is convolutional neural network. The inputof the machine learning model 20 is a vector of size N representing thechannel frequency response of a communication line 4, and the output ofthe machine learning model 20 is a pair of lengths [L_(long) L_(short)]representing an estimation of the length of the bridged tap(s)associated with the communication line 4.

The machine learning model 20 comprises a first convolutional layer 21,a first pooling layer 22, a second convolutional layer 23, a secondpooling layer 24 and a regression layer 25, arranged in series.

The first convolutional layer 21 comprises a plurality of filters ofsize 1×D₁. The second convolutional layer 22 comprises a plurality offilters of size 1×D₂. The H log curve of an impaired line typicallypresents a repetition of patterns indicative of the impairment type, forexample of a bridged tap. The combination of two convolutional layersallows detecting the patterns and their repetition. For an input curvewith N=512, example sizes are D₁<=8 and D₂>=10. Another important aspectof convolutional filtering is the number of filters in use. From themachine learning best practice theory, lower is the complexity of amodel, higher is its ability to generalize. As there is a directrelationship between the number of filters in use toward the number offeatures to combine and therefore toward complexity, the lower, thebetter. However, we would like anyway to extract enough features to beable to perform an accurate prediction. There is a trade-off thereforeto make. A preferred design is therefore between 8 and 16 fundamentalshapes, but not much. Proposing more than 16 filters, as this is commonin computer vision, is not relevant here. By contrast, proposing fewerfilters (e.g. <4) will not allow to detect or make the distinctionbetween some of the fundamental shapes, making impossible to detect orseparate the different impairments.

The pooling layers 22 and 24 allow reducing the size of the featuresmaps and therefore the complexity of the model. This comes from the factthat, as convolution mitigate neighboring values, there is a redundancyof information between neighboring outputs. There is therefore at thisstage opportunity to reduce the model complexity. Knowing that the finaloutput consists only in the prediction of a pair of scalar value(L_(long) and L_(short)) and knowing that there is an intrinsicsmoothing effect, the pooling layers 22 and 24 may perform fairly highpooling (down sampling), usually by a 1:X ratio, where X is usuallyequal or smaller than the size of the filter. In our case, this means apooling <1:8 in the first pooling layer 22, and of <1:10 in the secondpooling layer 24.

The regression layer 25 is also made of neurons and belong to the sameneural network, as visible in FIG. 5. Compared to the convolutionalpart, the purpose is to establish a relationship between all thefeatures and the pair [L_(long) L_(short)] to predict. At that stage,there is therefore no convolutional filtering nor pooling, but onlyconnections between all neurons toward other layer neurons. Theregression layer 25 may be a fully-connected part comprising an inputlayer, a hidden layer and an output layer. The input layer receives thefeatures provided by the convolutional part (convolutional layers 21,23, pooling layers 22, 24). The output layer comprises a pair of neuronswhich outputs the values [L_(long) L_(short)].

Beside these aspects, usually the major difference between the neuronalmodel proposed for feature extraction and for regression resides in thedifference between the activation function. Typically, Rectified LinearUnit (ReLU) activation functions are used to perform convolution usingneurons (which is a linear process), while non-linear activationfunctions are preferred for regression purpose. This is the preferredoption. However, as the coefficient to predict is continuously andfairly uniformly distributed, this makes no sense to opt for saturatingactivation functions like SoftSign or Tan H. In that sense, it has beenpreferred to opt for eLU function for our use-case.

Finally, the main tuning factor in this fully connected stage is theamount of neurons in-use. Between the number of feature and the pair ofscalar output values, a fairly low number of neurons (<128) may beenchosen. From a domain expertise, there is no valid reason at this stageto introduce extra complexity in the model by adding extra layers. Asingle regression layer is therefore chosen and will be finallyconnected to the output layer consisting in two neurons outputting thevalues L_(long) and L_(short) to predict. More generally, the outputlayer comprises M neurons for M lengths.

FIG. 6 is a flowchart of a method executed by the monitoring device 6.

The monitoring device 6 obtains data specifying the channel frequencyresponse CFR of a communication line 4 (step T1). For example, themonitoring device 6 collects channel frequency responses CFR from accessnodes 2 on a regular basis, e.g. every 15 minutes.

Then, the monitoring device 6 determines, based on the channel frequencyresponse CFR, whether the communication line 4 is affected by one ormore bridged taps (Step T2). Various techniques are known for detectingbridged taps, including deep-learning based techniques. The followingsteps may be performed in response to detecting bridged tap(s).

The monitoring device 6 determines a pair of lengths [L_(long)L_(short)] based on the channel frequency response CFR (step T3), byusing the trained machine learning model. The pair of lengths [L_(long)L_(short)] represents an estimation of the lengths of bridged tapsaffecting the communication line 4. The machine learning model mayoutput L_(short)=0, which means it is estimated that only one bridgedtap is present.

The bridged tap lengths may be used for various functions. When bridgedtap length estimation is performed for a plurality of communicationlines, statistics may be determined and displayed to represent technicalinformation about the communication network 1. Also, the bridged taplengths may be used for domain separation and repair action selection.

For example, in some embodiments, the monitoring device 6 determines,based on the pair [L_(long) L_(short)], a location of the bridged taps(Step T4). The location may be specified by one of a plurality ofdomain, for example operator domain, street/field domain, customerdomain. Known domain separation techniques allow determining a domainbased on a bridged tap length. The monitoring device 6 may determine arepair action based on the domain separation (Step T5). For example, therepair action comprises sending a technician to the specified location,to remove the bridged tap.

Steps T1 to T5 may be performed for a plurality of communication lines4. Accordingly, the bridged tap lengths and domain may be determined fora plurality of communication line 4. This allows comparing thecost/benefit of a repair action for respective communication lines 4 orgroups of communication lines 4.

FIG. 7 is a block diagram representing the structural architecture of anapparatus 30. The apparatus 30 may correspond to one of the apparatus 5,the monitoring device 6 and the configuration device 7.

The apparatus 30 comprises a processor 31 and a memory 32. The memory 32stores computer program code P. The memory 32 and the computer programcode P are configured for, with the processor 31, causing the apparatus30 to perform, at least in part, the method described with reference toFIG. 4 and/or FIG. 6.

In the context of this description, a machine learning model is afunction for outputting an output based on an input, which depends ontrainable parameters. An example of machine learning model is a neuralnetwork, with weights and biases as parameters. Training the machinelearning model is the task of determining the parameters of the modelbased on training data.

It should be noted that although examples of methods have been describedwith a specific order of steps, this does not exclude otherimplementations. In particular, the described steps may be executed inanother order, partially or totally in parallel . . . .

It is to be remarked that the functions of the various elements shown inthe figures may be provided through the use of dedicated hardware aswell as hardware capable of executing software in association withappropriate software. When provided by a processor, the functions may beprovided by a single dedicated processor, by a single shared processor,or by a plurality of individual processors, some of which may be shared,for example in a cloud computing architecture. Moreover, explicit use ofthe term “processor” should not be construed to refer exclusively tohardware capable of executing software, and may implicitly include,without limitation, digital signal processor (DSP) hardware, networkprocessor, application specific integrated circuit (ASIC), fieldprogrammable gate array (FPGA), read only memory (ROM) for storingsoftware, random access memory (RAM), and non-volatile storage. Otherhardware, conventional and/or custom, may also be included. Theirfunction may be carried out through the operation of program logic,through dedicated logic, through the interaction of program control anddedicated logic, or even manually, the particular technique beingselectable by the implementer as more specifically understood from thecontext.

It should be further appreciated by those skilled in the art that anyblock diagrams herein represent conceptual views of illustrativecircuitry embodying the principles of the invention. Similarly, it willbe appreciated that any flow charts represent various processes whichmay be substantially represented in computer readable medium and soexecuted by a computer or processor, whether or not such computer orprocessor is explicitly shown.

While the principles of the invention have been described above inconnection with specific embodiments, it is to be clearly understoodthat this description is made only by way of example and not as alimitation on the scope of the invention, as defined in the appendedclaims.

1. An apparatus comprising a memory configured to store executable code;and a processor configured to execute the executable code and cause theapparatus to perform the operations of generating a dataset specifying,for each communication line of a plurality of communication lines, achannel frequency response of a respective communication line of theplurality of communication lines, the communication line having one ortwo bridged taps, and a set of M lengths of bridged taps associated withthe respective communication line, with M greater than one, andtraining, based on the dataset, a machine learning model, the machinelearning model configured for determining, based on the channelfrequency response of the respective communication line, a set of Mlengths of bridged taps associated with the respective communicationline.
 2. The apparatus according to claim 1, wherein the training themachine leaning model comprises updating parameters of the machinelearning model based on an error representative of a relative comparisonbetween target lengths and predicted lengths.
 3. The apparatus accordingto claim 1, wherein the training the machine leaning model comprisesupdating parameters of the machine learning model based on an errorrepresentative of a comparison between target lengths and predictedlengths and of a length distribution associated with a communicationnetwork.
 4. The apparatus according to claim 3, wherein the processor isconfigured to further cause the apparatus to perform determining saidlength distribution based on iteratively, updating parameters of themachine learning model based on the dataset and a current estimation ofthe length distribution, determining lengths based on the updatedmachine learning model and the channel frequency responses of theplurality of communication lines of a real communication network, andupdating the current estimation of the length distribution based on thedetermined lengths.
 5. The apparatus according to claim 1, wherein thegenerating the dataset comprises determining the channel frequencyresponses based on circuit simulation.
 6. The apparatus according toclaim 1, wherein the generating the dataset comprises determining thechannel frequency responses for a plurality of communication lineshaving a single wire bridged tap and for a plurality of communicationlines having a double wire bridged tap.
 7. The apparatus according toclaim 1, wherein the generating the dataset comprises determining thechannel frequency responses for a plurality of communication lineshaving an open-ended bridged tap and for a plurality of communicationlines having a close-ended bridged tap.
 8. The apparatus according toclaim 1, wherein the generating the dataset comprises determining thechannel frequency responses for a plurality of communication lineshaving only one bridged tap and for a plurality of communication lineshaving at least two bridged taps.
 9. The apparatus according to claim 1,wherein the processor is configured to further cause the apparatus toperform: obtaining the channel frequency response of the respectivecommunication line, and determining a set of M lengths of bridged tapsassociated with the respective communication line, based on the channelfrequency response and the trained machine learning model.
 10. Acomputer-implemented method comprising: generating a dataset specifying,for each communication line of a plurality of communication lines, achannel frequency response of a respective communication line of theplurality of communication lines having one or two bridged taps, and aset of M lengths of bridged taps associated with the respectivecommunication line, with M greater than one, training, based on thedataset, a machine learning model, the machine learning model configuredfor determining, based on the channel frequency response of therespective communication line, a set of M lengths of bridged tapsassociated with the respective communication line.
 11. The methodaccording to claim 10, comprising: deploying the trained machinelearning model in another apparatus.
 12. An apparatus obtained by themethod of claim 11, comprising a processor configured to: obtain thechannel frequency response of the respective communication line,determine a set of M lengths of bridged taps associated with therespective communication line, based on the channel frequency responseand the trained machine learning model.
 13. A computer-implementedmethod for monitoring a communication network, comprising: obtaining thechannel frequency response of the respective communication line,determining a set of M lengths of bridged taps associated with therespective communication line, based on the channel frequency responseand a trained machine learning model generated by the method of claim10.
 14. A non-transitory computer-readable medium storing instructions,which when executed by a processor, causes an apparatus including theprocessor to perform the method of claim 10.